docstr: Draw random samples. Samples are in the original (untransformed) space. They must be
  transformed before being passed to a model or minimizer via :meth:`transform`
  
  Parameters
  ----------
  n_samples: Int, default=1
      Number of samples to be drawn from the space
  random_state: Int, RandomState, or None, default=None
      Set random state to something other than None for reproducible results
  
  Returns
  -------
  List
      Randomly drawn samples from the original space. Will be a list of lists, of shape
      (`n_samples`, :attr:`n_dims`)
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code: def rvs(self, n_samples=1, random_state=None):
      """Draw random samples. Samples are in the original (untransformed) space. They must be
      transformed before being passed to a model or minimizer via :meth:`transform`
  
      Parameters
      ----------
      n_samples: Int, default=1
          Number of samples to be drawn from the space
      random_state: Int, RandomState, or None, default=None
          Set random state to something other than None for reproducible results
  
      Returns
      -------
      List
          Randomly drawn samples from the original space. Will be a list of lists, of shape
          (`n_samples`, :attr:`n_dims`)"""
      rng = check_random_state(random_state)
  
      #################### Draw ####################
      columns = []
  
      for dim in self.dimensions:
          new_val = None
          try:
              if sp_version < (0, 16):
                  new_val = dim.rvs(n_samples=n_samples)
              else:
                  new_val = dim.rvs(
                      n_samples=n_samples, random_state=rng
                  )
          except (
              TypeError
          ):  # `'<' not supported between instances of 'Version' and 'str'`
              new_val = dim.rvs(n_samples=n_samples, random_state=rng)
          finally:
              columns.append(new_val)
  
      #################### Transpose ####################
      rows = []
      # TODO: Use `np.transpose`? Might that screw up the dimension types (mostly `Categorical`)
      for i in range(n_samples):
          r = []
          for j in range(self.n_dims):
              r.append(columns[j][i])
  
          rows.append(r)
  
      return rows
  
imports: from hyperparameter_hunter.space.dimensions import Dimension, Real, Integer, Categorical
  from hyperparameter_hunter.utils.general_utils import short_repr
  from functools import reduce
  import numbers
  import numpy
  from sys import maxsize
  from sklearn.utils import check_random_state
  from sklearn.utils.fixes import sp_version
label_desc: Use image node_id 7 for calling the Space obj's underlying member method code with example usage: obj.rvs(n_samples, random_state) and returns: rows